Compression of digital images of scanned documents

ABSTRACT

A first aspect of the invention relates to a method for creating a binary mask image from an a inputted digital image of a scanned document, comprising the steps of creating a binarized image by binarizing the inputted digital image, detecting first text regions representing light text on a dark background, and inverting the first text regions, such that the inverted first text regions are interpretable in the same way as dark text on a light background. A second aspect of the invention relates to a method for comparing in a binary image a first pixel blob with a second pixel blob to determine whether they represent matching symbols, comprising the steps of detecting a line in one blob not present in the other and/or determining if one of the blobs represents an italicized symbol where the other does not.

TECHNICAL FIELD

The present invention relates to methods, algorithms and computerreadable program products for compressing inputted digital images ofscanned documents.

BACKGROUND ART

ITU-T have defined in their Recommendation T.44 the Mixed Raster Content(MRC) model. By using this model, it would be possible to compress colorand grayscale document images with a high compression rate, a goodlegibility of the text and a good rendering of the pictures. The MRCModel divides the document image into 3 layers: the binary mask layer,the foreground layer and the background layer. The mask layer is abinary image, the background and foreground layers are color (orgrayscale) images. An ON pixel in the binary mask layer indicates that,when decompressing, the color (or grayscale) has to be taken from theforeground layer. An OFF pixel in the binary mask layer indicates that,when decompressing, the color (or grayscale) has to be taken from thebackground layer. However, ITU-T T.44 does not specify the method of thedivision into layers.

From U.S. Pat. No. 5,778,092 a first technique for compressing a coloror gray scale pixel map representing a document is known, correspondingto the MRC model. The pixel map is decomposed into a three-planerepresentation comprising a reduced-resolution foreground plane, areduced-resolution background plane, and a high-resolution binaryselector plane. The foreground plane contains the color or gray scaleinformation of foreground items such as text and graphic elements. Thebackground plane contains the color or gray scale information for the“background” of the page and the continuous tone pictures that arecontained on the page. The selector plane stores information forselecting from either the foreground plane or background plane duringdecompression. Each of the respective planes is compressed using acompression technique suitable for the corresponding data type.

From U.S. Pat. No. 6,731,800 another technique is known for compressingscanned, colored and gray-scale documents, in which the digital image ofthe scanned document is divided into three image planes, namely aforeground image, a background image and a binary mask image. The maskimage describes which areas of the document belong to the foreground andwhich to the background. In order to generate the mask image, a locallyvariable threshold value image is generated from the defined reducedoriginal document with an adaptive threshold method, and brought backonce again to the size of the original document. With this technique,also inverse text (light text on a dark background) can be detected. Theinverse text is detected by the concept of “holes”. A “hole” is aforeground region or blob which touches a different foreground regionwhich has already been entered. This method requires a lot of memorysince all blobs have to be tracked and is time consuming since it has tobe checked if the blobs are touching each other. In addition both the“black” blobs and the “white” blobs have to be recorded.

From U.S. Pat. No. 6,748,115 an image compression technique is known,which employs selecting a gray level threshold value for converting agray level digital image input into a bi-level input which minimizesweak connectivity, wherein weak connectivity comprises a checkerboardpattern found in a 2×2 array or neighborhood of pixels. The thresholdvalue for the conversion is determined by traversing the array of pixelscomprising the document in a single path, examining successive 2×2neighborhoods and incrementing a plus register for the gray level valuewhich a checkerboard pattern first appears and incrementing a minusregister for the gray level value at which the checkerboard pattern nolonger exists.

These image compression techniques however have the disadvantage thatthe achieved compression rates are insufficient. Often also the qualityof the reconstructed image, e.g. the legibility of the text or therendering of the pictures is affected by the compression technique.

From U.S. Pat. No. 5,835,638 a method and apparatus are known forcomparing symbols extracted from binary images of text for classifyinginto equivalence classes. A Hausdorff-like method is used for comparingsymbols for similarity. When a symbol contained in a bitmap A iscompared to a symbol contained in a bitmap B, it is determined whetheror not the symbol in bitmap B fits within a tolerance into a dilatedrepresentation of the symbol in bitmap A with no excessive density oferrors and whether the symbol in bitmap A fits within a tolerance into adilated representation of the symbol in bitmap B with no excessivedensity of errors. If both tests are passed, an error density check isperformed to determine a match.

This known symbol comparison method has the disadvantage that in manycases a match may be returned where in fact a mismatch occurs.

DISCLOSURE OF THE INVENTION

It is an aim of the present invention to provide an image compressiontechnique for scanned documents with which a higher compression rate canbe achieved without affecting the quality of the reconstructed image.

In particular, it is an aim of the invention to provide an imagecompression technique which does substantially not compromise on thelegibility of the text or the rendering of the pictures.

It is another aim of the invention to provide an image compressiontechnique which is very flexible in adjusting the trade-off betweencompactness and quality.

It is another aim of the invention to provide an image compressiontechnique suitable for any type of documents, e.g. documents which maycontain text elements of different colors and intensities and/ordocuments containing text elements placed on backgrounds of differentcolors and intensities or on non-uniform backgrounds such as a watermarkor a photo.

In particular, it is an aim of the invention to provide an imagecompression technique suitable for documents containing light text on adark background.

In particular, it is an aim of the invention to provide an imagecompression technique with which horizontal and vertical graphical linescan be decompressed with high quality.

It is another aim of the invention to provide an image compressiontechnique in which no document-specific parameter needs to be set for aparticular type of document.

It is a further aim of the invention to provide an image binarizationtechnique which is less time consuming and more memory efficient.

It is a further aim of the invention to provide an image binarizationtechnique with improved edge detection.

It is a further aim of the invention to provide a symbol comparisontechnique which generates the minimum number of model classes,substantially without substitution errors.

These and other aims are achieved according to the invention with themethods and computer program products showing the technicalcharacteristics of the claims.

In a first aspect of the invention, a method is proposed for creating abinary mask image from an inputted digital image of a scanned document.The method comprises the steps of: (a) creating a binarized image bybinarizing said inputted digital image, (b) detecting in said binarizedimage first text regions representing light text on a dark background insaid inputted digital image, and (c) inverting said first text regionsin said binarized image, such that the inverted first text regions areinterpretable in the same way as dark text on a light background. Bymeans of these steps, inverted text (light text on a dark background) isdetectable in a more efficient way with respect to the prior art, inparticular at a higher speed and requiring less memory. By theinversion, the inverse text becomes interpretable in the same way asnormal text (dark text on a light background), so no special steps oralgorithms are needed to detect the inverse text and place it in thebinary mask.

The method of the first aspect of the invention can for example beapplied in image compression techniques using the MRC model. In suchtechniques, recording the inverse text in the same way as normal texthas the advantage that the inverse text can be put in the foreground andnot in the background. As a result, the inverse text can be compressedby a symbol-based compression technique which can lead to a highercompression rate. Furthermore, the legibility of the inverse text in thereconstructed image can be enhanced, since it is reconstructed on thebasis of the foreground and background images and not only thebackground image, which usually has a low resolution and is compressedwith a low quality.

The method of the first aspect of the invention can for example beapplied in text recognition techniques. In such techniques, recordingthe inverse text in the same way as normal text has the advantage thatthe inverse text can be recognized along with normal text and isafterwards also text searchable.

In preferred embodiments, the method of the first aspect of theinvention further comprises one or more of the following steps: (d)detecting in said binarized image second text regions representing darktext on a light background in said inputted digital image, and (e)eliminating from the binarized image text regions that represent noactual text but for example picture elements coming from picture partsin the inputted image.

The creation of the binarized image by binarizing the inputted digitalimage preferably comprises the following steps: (a1) building agrayscale image from said inputted digital image, (a2) detecting edgesin said grayscale image, thereby building an edge binary imagecontaining edge pixels and non-edge pixels, (a3) determining thresholdvalues for each of said edge pixels on the basis of surrounding pixelsand giving said non-edge pixels a null threshold value, thereby buildinga threshold grayscale image, (a4) determining threshold values for eachof said non-edge pixels touching the edge pixels on the basis ofsurrounding threshold values, (a5) scaling said threshold grayscaleimage by keeping the maximum threshold values, (a6) propagating thethreshold values from pixels having a positive value to pixels having anull value, and (a7) building a first binary image on the basis of saidgrayscale image and said scaled threshold grayscale image. These stepshave the advantage that the threshold which is used for building thefirst binary image varies in order to detect more elements with variousbrightnesses and contrasts in the inputted digital image. In an MRCmodel compression technique, this can enhance the quality of thereconstructed image after decompression.

The step (a2) of detecting edges of text symbols in said imagepreferably comprises the use of a canny edge algorithm for said edgedetection of text symbols. A canny edge algorithm uses a multiple stagealgorithm to detect a wide range of edges and is for example known fromJ. Canny, “A Computational Approach to Edge Detection”, IEEETransactions on Pattern Analysis and Machine Intelligence, Vol 8, No. 6,November 1986, which is herein incorporated by reference in itsentirety. The use of this algorithm can lead to a substantialimprovement in the detection and/or recognition of text symbols in animage.

More preferably, the creation of the binary image further comprises thefollowing steps: (a8) building a second binary image on the basis ofsaid grayscale image and said threshold grayscale image, and (a9)building said binarized image by combining said first and second binaryimages. These steps have the advantage that the threshold which is usedfor building the second binary image varies further in order to detecteven more elements with various brightnesses and contrasts in theinputted digital image. In an MRC model compression technique, this canenhance the quality of the reconstructed image after decompression.

In an embodiment, the creation of the binary mask image involvesreducing the resolution of the inputted digital image by a binary maskresolution reduction factor. In this way, the binary mask resolution canfor example be user-adjustable in an MRC model compression technique,depending on the desired quality of the reconstructed image.

In a second aspect of the invention which may or may not be combinedwith the other aspects of the invention, a method is proposed forcomparing in a binary image a first pixel blob with a second pixel blobto determine whether they represent matching symbols, comprising thesteps of: (f) dilating the first blob and checking if the second blobfits inside the dilated first blob, and (g) dilating the second blob andchecking if the first blob fits inside the dilated second blob. Thecomparison method further comprises at least one of the following steps:(h) detecting a line in one of the first and second blobs not present inthe other, (i) determining if one of the first and second blobsrepresents an italicized symbol where the other does not. Steps (h) and(i) can effectively reduce the number of erroneous symbol matches or, inother words, reduce the risk that mismatching symbols would be detectedas matching symbols.

Preferably in the method according to the second aspect of theinvention, step (h) comprises checking for N×N crosses in which one linein one of the blobs has a different color from that of one line in theother blob, wherein N is a number of bits, preferably 3.

Preferably in the method according to the second aspect of theinvention, step (i) comprises checking if the number of black pixelswhich the first and second blobs have in common is above a predeterminedthreshold. This predetermined threshold preferably equals 80-90%, morepreferably about 85% of the total amount of pixels in a blob, but otherthresholds may also be used if deemed suitable by the person skilled inthe art.

All above mentioned aspects of the invention may be part of a furtheraspect of the invention, namely a compression method for compressingsaid inputted digital image of said scanned document, said compressionmethod comprising the steps of: (j) segmenting said inputted digitalimage into multiple image layers comprising a foreground imagecontaining color information for foreground elements of said document, abackground image containing color information for background elements ofsaid document and said binary mask image for selecting between pixels insaid foreground image and said background image upon decompressing saidcompressed digital image, and (k) compressing each of the image layersby means of a suitable compression technique, thereby obtaining acompressed digital image.

Preferably in this further aspect, the creation of said binary maskimage involves reducing the resolution of the inputted digital image bya binary mask resolution reduction factor. The binary mask resolutioncan for example be user-adjustable, depending on the desired quality ofthe reconstructed image.

Preferably in this further aspect, the foreground and background imagesare built by reducing the resolution by respectively a foregroundresolution reduction factor and a background resolution reductionfactor. The foreground and background resolutions can for example beuser-adjustable, depending on the desired quality of the reconstructedimage.

Preferably in this further aspect, the compression comprises the stepsof (k1) compressing said foreground and background images by means of animage compression technique, such as for example JPEG 2000 or any otherknown to the skilled person, and (k2) compressing said binary mask imageby means of a symbol-based compression technique.

BRIEF DESCRIPTION OF THE DRAWINGS

The invention will be further elucidated by means of the followingdescription and the appended figures.

FIG. 1 shows a general flowchart of an MRC model compression algorithmaccording to the invention.

FIG. 2 shows a detailed flowchart of the segmentation step in theflowchart of FIG. 1.

FIG. 3 shows a detailed flowchart of the binarization step in theflowchart of FIG. 2.

FIG. 4 shows a detailed flowchart of the foreground elements selectionstep in the flowchart of FIG. 2.

FIG. 5 shows a detailed flowchart of the large blob elimination andwhite blob inversion step in the flowchart of FIG. 4.

FIG. 6 shows a variation of the flowchart of FIG. 5 in which in additionhorizontal and vertical graphical elements are kept.

FIG. 7 shows a detailed flowchart of the background image building stepin the flowchart of FIG. 2.

FIG. 8 shows a detailed flowchart of the foreground image building stepin the flowchart of FIG. 2.

FIG. 9 shows a flowchart of a symbol comparison algorithm according tothe invention.

FIG. 10 shows a visualization of steps of the symbol comparisonalgorithm of FIG. 9.

FIG. 11 shows an example of an inputted color image, compressible bymeans of the method of FIGS. 1-8.

FIG. 12 shows the image of FIG. 11 after the binarization step.

FIG. 13 shows a binary mask image built from the image of FIG. 11.

FIG. 14 shows a background image built from the image of FIG. 11.

FIG. 15 shows a foreground image built from the image of FIG. 11.

FIG. 16 shows a reconstructed image built by means of the images ofFIGS. 13-15.

FIGS. 17-21 show stepwise how the binary mask image of FIG. 13 is builtfrom the binarized image of FIG. 12.

MODES FOR CARRYING OUT THE INVENTION

The present invention will be described with respect to particularembodiments and with reference to certain drawings but the invention isnot limited thereto but only by the claims. The drawings described areonly schematic and are non-limiting. In the drawings, the size of someof the elements may be exaggerated and not drawn on scale forillustrative purposes. The dimensions and the relative dimensions do notnecessarily correspond to actual reductions to practice of theinvention.

Furthermore, the terms first, second, third and the like in thedescription and in the claims, are used for distinguishing betweensimilar elements and not necessarily for describing a sequential orchronological order. The terms are interchangeable under appropriatecircumstances and the embodiments of the invention can operate in othersequences than described or illustrated herein.

Moreover, the terms top, bottom, over, under and the like in thedescription and the claims are used for descriptive purposes and notnecessarily for describing relative positions. The terms so used areinterchangeable under appropriate circumstances and the embodiments ofthe invention described herein can operate in other orientations thandescribed or illustrated herein.

The term “comprising”, used in the claims, should not be interpreted asbeing restricted to the means listed thereafter; it does not excludeother elements or steps. It needs to be interpreted as specifying thepresence of the stated features, integers, steps or components asreferred to, but does not preclude the presence or addition of one ormore other features, integers, steps or components, or groups thereof.Thus, the scope of the expression “a device comprising means A and B”should not be limited to devices consisting only of components A and B.It means that with respect to the present invention, the only relevantcomponents of the device are A and B.

As used herein, a “color image” is intended to mean or a color rasterimage, i.e. a pixel map with each pixel representing a color value.

As used herein, a “grayscale image” is intended to mean a pixel map witheach pixel representing an intensity value.

As used herein, a “binary image” or a “binarized image” is intended tomean a bitonal image, for example a black&white image, i.e. a pixel mapwith each pixel representing a binary value (ON or OFF, 1 or 0, black orwhite).

As used herein, “binarization” is intended to refer to an operation thattransforms a color or grayscale image into a binary image.

As used herein, an “AND” operation of 2 binary images is intended torefer to an operation that makes the logical AND of the correspondingpixels in the 2 source images and puts the result in the destinationimage.

As used herein, an “OR” operation of 2 binary images is intended torefer to an operation that makes the logical OR of the correspondingpixels in the 2 source images and puts the result in the destinationimage.

As used herein, an “XOR” operation of 2 binary images is intended torefer to an operation that makes the logical XOR of the correspondingpixels in the 2 source images and puts the result in the destinationimage.

As used herein, an “inversion” of a binary image is intended to refer toan operation that inverts each pixel of the source image and puts theresult in the destination image.

As used herein, “dilation” of a binary image is intended to refer to anoperation that for each black pixel adds an N×N black pattern on thedestination image with the pattern centered at the correspondinglocation in the destination image. For example, dilation by a 3×3 blackpattern means an operation that for each black pixel adds a 3×3 blackpattern on the destination image.

As used herein, “dilation” of a grayscale image is intended to refer toan operation that for each pixel searches for the value of the darkestpixel in an N×N (e.g. 3×3) square centered on this pixel and puts thisvalue on the corresponding pixel of the destination image.

As used herein, a “blob” in a binary image is intended to refer to agroup of connected black or white pixels.

In the following, aspects of the invention will be described using theexample of an image compression method. Note that many of the describedalgorithms may also be applied in other methods, for example for textrecognition or other. Furthermore, many modifications may be made to thedescribed steps and algorithms without departing from the scope of theinvention.

The compression method shown in FIG. 1 is based on the MRC model inwhich the inputted color or grayscale image is segmented in three layersafter which each layer is compressed separately by means of a suitablecompression technique. In particular, an inputted color image 1 issegmented into a background image 5, a binary mask image 6 and aforeground image 7 by means of a segmentation algorithm 100 which takesas parameters a background resolution reduction factor 2, a foregroundresolution reduction factor 3 and a binary mask resolution reductionfactor 4. The mask image 6 is a binary image, the background andforeground images 5, 7 are color or grayscale images (depending onwhether the inputted image 1 is color or grayscale). An ON pixel in thebinary mask image 6 indicates that, when decompressing, the color (orgrayscale) has to be taken from the foreground image 7. An OFF pixel inthe binary mask image 6 indicates that, when decompressing, the color(or grayscale) has to be taken from the background image 5.Subsequently, the background image 5 is compressed by means of an imagecompression technique 300 (such as for example JPEG 2000) which takes asparameter a background quality 8, the binary mask image 6 is compressedby a symbol-based compression technique (such as for example JBIG2)which takes as parameter a mask quality 9 and the foreground image 7 iscompressed by means of an image compression technique 500 (such as forexample JPEG 2000) which takes as input a foreground quality 10.Finally, the compressed images are encapsulated in a document readableformat such as for example PDF, yielding a compressed image 11.

The segmentation algorithm 100 is detailed by means of FIGS. 2-8, whichshow its steps and sub-algorithms. In general, as shown in FIG. 2, thesegmentation comprises the following steps. The inputted image 1 isbinarized with an adaptive local algorithm 110, which results in abinarized image 123 from which the foreground elements are selected instep 125 to build a binary mask image 6. This binary mask image 6 isused to build, on the basis of the inputted image 1, the backgroundimage 5 in step 170 and the foreground image 7 in step 180. Steps 170and 180 respectively take into account the background resolutionreduction factor 2 and the foreground resolution reduction factor 3.Step 126 is an optional step to reduce the resolution of the binary maskimage 6 on the basis of the binary mask resolution reduction factor 4.

As shown in FIG. 3, the binarization of the inputted image 1 comprisesan adaptive local algorithm 110 with the following steps. In step 111,the inputted image 1 (if in color) is transformed into a grayscale imageby calculating the pixels intensities. This grayscale image may besmoothed (e.g. by a Gaussian filter) in order to reduce the noises.

Next, in step 112, edges are detected in the grayscale image by means ofthe Canny Edge detection algorithm. This was developed by John F. Cannyin 1986 and uses a multiple stage algorithm to detect a wide range ofedges, and is for example described in J. Canny, “A ComputationalApproach to Edge Detection”, IEEE Transactions on Pattern Analysis andMachine Intelligence, Vol 8, No. 6, November 1986, which is hereinincorporated by reference in its entirety. The algorithm uses twothresholds “Thigh” and “Tlow” in order to avoid breaking up an edgecontour. An edge contour starts with a pixel whose gradient is greaterthan Thigh but can continue even for pixels whose gradient is lower thanThigh but greater than Tlow. According to the invention, typical valuesfor Tlow and Thigh are respectively 32 and 40 for a 1-byte grayscaleimage. The canny edge detection algorithm is used to detect the edges ofthe text and graphic elements in the grayscale image. By doing so, alsoedges in pictures are detected, but this is not a problem since thepicture elements can be filtered afterwards (step 160, see below). Thecanny edge detector produces a binary image in which only the edgepixels are set to 1.

With respect to other edge detection algorithms, the Canny edgedetection algorithm offers the following advantages:

-   -   good detection: the algorithm marks as many real edges in the        image as possible;    -   good localization: edges marked are as close as possible to the        edge in the real image;    -   minimal response: a given edge in the image is only marked once,        and where possible, image noise does not create false edges.

Next in step 113, a threshold value is calculated for each edge pixel ofthe edge binary image output by the Canny edge algorithm 112. Thethreshold is to half the sum of a minimum and maximum value. The minimumvalue is the minimum value of a 3×3 square centered on the examinedpixel. The maximum value is the maximum value of a 3×3 square centeredon the examined pixel. Non-edge pixels receive a threshold value of 0.

Next in step 114, threshold values are assigned to non-edge pixelstouching edge pixels. The threshold values are copied from the input tothe output. For non-edge pixels (value=0), A 3×3 square is centered oneach pixel. The sum of the threshold values for the pixels in thissquare is calculated and divided by the number of edge pixels if thereare any. This value is copied on the output image. The output of step114 is a threshold grayscale image.

Next in step 115, this threshold grayscale image is scaled by an integerfactor, preferably 4. The output image is initialized at 0. For eachoutput pixel, the values of the corresponding input pixels are added anddivided by the number of non-zero values if there are any.

Next in step 116, the thresholds are averaged with the values of theneighbors. A 3×3 square is centered on each pixel for which the value isdifferent from 0. The sum of the threshold values for the pixels in thissquare is calculated and divided by the number of non-zero values ifthere are any. This value is copied on the output image.

Next in step 117, the threshold values are propagated to pixels havingno threshold values. Firstly, threshold values are assigned to non-zerovalue pixels touching zero value pixels in about the same way as in step114. Secondly, a 2 pass propagation algorithm is used. In a 1st pass theimage is scanned from left to right and from top to bottom. For zerovalue pixels, the value is put to that of the neighbor pixel with thesmallest non-zero value. In a 2nd pass, the image is scanned from rightto left and from bottom to top. Again, for zero value pixels, the valueis put to that of the neighbor pixel with the smallest non-zero value.In this way all pixels receive a threshold value.

Next in step 118 a first binary image 119 is built by combination of thegrayscale image output from step 111 and the threshold scaled grayscaleimage output from step 117. The value of each pixel of the grayscaleimage is compared to the threshold value of the corresponding pixel inthe scaled threshold image. A value of 1 or 0 is set in the first binaryimage 119 depending on whether the pixel value is below or above thethreshold.

In step 120 a second binary image 121 is built by combination of thegrayscale image output from step 111 and the threshold grayscale imageoutput from step 114. The value of each pixel of the grayscale image iscompared to the threshold value of the corresponding pixel in thethreshold image. A value of 1 or 0 is set in the second binary image 121depending on whether the pixel value is below or above the threshold.

Finally in step 122 of the binarization algorithm 110 an OR is made ofthe first binary image 119 and the second binary image 121 to generatethe binarized image 123.

The binarized image 123 contains in addition to text and graphicelements, elements coming from picture parts in the inputted image 1. Itis preferred that those elements are eliminated, so that they do notoccur in the binary mask image 6. Furthermore, text elements in white onblack are inverted to achieve that they become interpretable in the sameway as black on white text, which is very advantageous in the furthertreatment of the binary mask image 6. These steps are carried out by aselection algorithm 125, which is shown in FIG. 4.

By means of the sub-algorithms 130 or 140, large blobs are eliminatedfrom the binarized image 123 and the white blobs are inverted. Thedifference between the two is that in sub-algorithm 140 steps are addedfor keeping horizontal and vertical graphical elements,

Sub-algorithm 130, shown in FIG. 5, comprises the following steps. Instep 131, only large black blobs are kept, i.e. blobs with a number ofpixels above a given minimum. Next, the image with those kept blobs isinverted in step 132. In step 133 a logical AND is made of this invertedimage and the binarized image. These steps are repeated for the imageoutput of step 132: again only large black blobs are kept in step 134,the image with those kept blobs is inverted in step 135 and an AND ismade in step 136 with the image output of step 132. Finally, an XOR ismade in step 137 of the image outputs of steps 133 and 136, resulting ina transformed binary image 138.

Sub-algorithm 140, shown in FIG. 6, comprises the following steps. Bymeans of elimination step 141 and XOR step 142 horizontal and verticalgraphical elements are separated from the binarized image 123. Steps143-145 correspond to steps 131-133 of the sub-algorithm 130. By meansof elimination step 146 and XOR step 147, further horizontal andvertical graphical elements are separated from the image output of step144. These separated graphical elements are combined in step 148 bymaking an OR of the outputs of XOR steps 142 and 147. Steps 149-151correspond to steps 134-136 of the sub-algorithm 130. Again, an XOR ismade in step 152 of the image outputs of the AND steps 145 and 151.Finally, a transformed binary image 157 is generated in step 156 bymaking an OR of the outputs of XOR steps 152 and OR step 148, puttingthe horizontal and vertical graphical elements back into the image.

The above mentioned steps for inverting the white on black text elementshave the advantage that it is not needed to store the descriptions(bitmaps or list of runs) of all the blobs of the document. This isneeded by prior art methods, especially in order to invert white onblack blobs by seeking for holes (blobs enclosed in other blobs). Inalgorithms 130 and 140 the blobs are processed as soon as they are foundand after a blob has been processed, its description is eliminated.

Returning to FIG. 4, the binary mask image 6 is generated from thetransformed binary image 138/157 by eliminating picture blobs (i.e.blobs relating to no actual text elements). This is achieved by afiltering step 160 which uses the Minimum Description Length Principle,which is known in the art. Here is decided for each blob whether it is aforeground or a background element. In applying the Minimum DescriptionLength principle, the strategy is to know whether the blob would bebetter compressed as being the foreground or as being part of thebackground. If the blob is in the background, it would be compressed inthe background image. If the blob is in the foreground, it would becompressed in the binary mask image and in the foreground image. So, thecost of the different compressions must be estimated. The estimation isdone by using simple models. The background model assumes that the colorchanges smoothly. The color of a pixel is not very different from itsneighbors. The cost is the sum of the errors between the pixel colorsand the local average color. The foreground model assumes that all thepixels of a blob have the same color. This is normally the case for textelements. The cost is the sum of the errors between the pixel colors andthe average color. The binary mask model assumes that compression costdepends of the perimeter of the blob. So, a blob is part of thebackground (and filtered) if:cost background<cost foreground+perimeter*factor

The factor is the only parameter and is tuned by testing a lot of pages.Here again, the blobs are processed as soon as they are found. After ablob has been processed, its description is eliminated.

As shown in FIG. 7, the background image 5 is generated by means of analgorithm 170 which takes as inputs the inputted image 1, the binarymask image 6 and the background resolution reduction factor 2, which maybe a user-definable parameter. In step 171, the binary mask image isdilated. In step 172, the resolution is reduced by the backgroundresolution reduction factor 2. Likewise in step 173 the resolution ofthe inputted image 1 is reduced by the background resolution reductionfactor 2. The resulting images are combined in step 174 to fill “empty”pixels. The color of the pixels of the reduced inputted image which areON in the reduced binary mask image is changed by using a 2-stepsequential algorithm. In a 1^(st) pass the 2 images are scanned fromleft to right and from top to bottom. When a “empty” pixel isencountered (OFF value in the reduced binary mask), a 3×3 square iscentered on it in the reduced inputted image and the sum of the colorvalues for the pixels in this square is calculated and divided by thenumber of non-empty pixels if there are any. This color value isassigned to the pixel in the reduced inputted image. In a 2^(nd) pass,the 2 images are scanned from right to left and from bottom to top.Again, when an “empty” pixel is encountered, a color value is assignedto it in the same way as in the 1^(st) pass.

As shown in FIG. 8, the foreground image 7 is generated by means of analgorithm 180 which takes as inputs the inputted image 1, the binarymask image 6 and the foreground resolution reduction factor 3, which maybe a user-definable parameter. In steps 181 and 182, the binary maskimage is subsequently inverted and dilated. In step 183, the resolutionis reduced by the foreground resolution reduction factor 2. Likewise instep 185 the resolution of the inputted image 1 is reduced by theforeground resolution reduction factor 2. The resulting images arecombined in step 186 to fill “empty” pixels. The color of the pixels ofthe reduced inputted image which are ON in the reduced binary mask imageis changed by using a 2-step sequential algorithm like in step 174.

An example of an image compressed by means of the method of FIGS. 1-8 isshown in FIGS. 11-16. FIG. 11 shows an inputted color image 1 which is ascanned document at 300 dpi. FIG. 12 shows the binarized image 123 afterthe binarization 110 and FIG. 13 shows the binary mask image 6 after theselection of foreground elements 125. The binary mask image is also at300 dpi, which means that in this case the binary mask resolutionreduction factor is set to 1 (no reduction). FIG. 14 shows thebackground image 5 which is at 100 dpi, meaning that the backgroundresolution reduction factor is set to 3. FIG. 15 shows the foregroundimage 7 which is at 50 dpi, meaning that the foreground resolutionreduction factor is set to 6. FIG. 16 shows the reconstructed image 12which is achieved after decompression of the compressed image 11. Sincethe binary mask image is at 300 dpi, the reconstructed image is also at300 dpi, i.e. the same resolution as the inputted image 1. From acomparison of FIGS. 11 and 16 it is clear that a high quality ofreconstructed images can be achieved with the compression method ofFIGS. 1-8.

FIGS. 17-21 show how the binary mask image 6 is built from the binarizedimage 123 by the foreground elements selection process 125 (see FIGS.4-6). FIG. 17 shows the binarized image 123 after separation of thevertical black line in the middle of the image, i.e. after step 141.FIG. 18 shows the binarized image 123 with only the large black blobskept and after inversion, i.e. after steps 143 and 144. FIG. 19 showsthe binarized image 123 without large black blobs, which is achieved byAND-ing the images of FIGS. 17 and 18 in step 145. FIG. 20 shows theresult after steps 146-151, i.e. an inverted image without large blackblobs. By XOR-ing these two images of FIGS. 19 and 20 in step 152, atransformed binary image 138/157 is achieved, which is the binarizedimage 123 in which large black blobs have been removed and white blobshave been inverted. This transformed image is shown in FIG. 21. It canbe seen that text regions representing light text on a dark backgroundor “inverted text” in the original image 1 is represented as black onwhite in the same way as dark text on a light background or “normaltext” in the original image 1. The binary mask image 6 of FIG. 13 isbuilt from the transformed image of FIG. 21 by filtering the pictureblobs in step 160.

FIG. 9 shows a symbol classification algorithm, in accordance with thesecond aspect of the invention, which may be part of the symbol-basedcompression algorithm 400 used for compressing the binary mask image 6.The symbol classification algorithm takes as input a binary image, suchas for example the binary mask image 6, and outputs a list of symbolsand a list of symbol occurrences. An item in the list of symbolsincludes a symbol ID and a symbol bitmap. An item in the list ofoccurrences includes the matching symbol ID and the position of theoccurrence in the page.

The symbol classification algorithm comprises the following steps. Insteps 403 and 404 it is determined whether a first pixel blob 401 fitswithin a dilation of a second pixel blob 402. If not, a “mismatch” isreturned. If so, in steps 405 and 406 it is determined whether thesecond pixel blob 402 fits within a dilation of the first pixel blob401. If not, a “mismatch” is returned. If so, it looks like a “match”,but two further checks are made to avoid errors. In steps 407 and 408,it is determined if one of the blobs 401, 402 has a line not present inthe other. More particularly, this involves checking for 3×3 crosses inwhich one line in one of the blobs has a different color from that ofthe other line in the other blob. In steps 409 and 411 it is determinedif one of the blobs 401, 402 represents an italicized symbol where theother does not. More particularly, this involves checking if the numberof black pixels which the first and second blobs 401, 402 have in commonis above a predetermined threshold 410. This predetermined thresholdpreferably equals 80-90%, more preferably about 85% of the total amountof pixels in a blob.

These steps are visualized in FIG. 10. The top row represents two pixelblobs 421 and 422 which fit into each other's dilations (steps 403-406),so the result 423 is a match. The middle row represents two pixel blobs431 and 432 which also fit into each other's dilations (steps 403-406),but are clearly not a match. This kind of errors is eliminated bychecking for 3×3 crosses 433, 434 in which one line in one of the blobshas a different color from that of the other line in the other blob(steps 407-408). In the cross 433 on the first blob 431 the verticalline 435 is black while the horizontal line 436 in the correspondingcross 434 on the second blob 432 is white, so the result 437 is amismatch. The bottom row represents two pixel blobs 441 and 442 whichalso fit into each other's dilations (steps 403-406), but are clearlynot a match either since blob 442 is a symbol in italics while blob 441is not. This kind of errors is eliminated by AND-ing the blobs 441 and442, the result of which is shown in 443 (the black pixels which are incommon are black in result 443) and checking if the number of pixels incommon is above threshold 410 (steps 409-411). In this example, thenumber of pixels in 443 would be below the threshold 410, so a mismatchis detected.

The invention claimed is:
 1. A method for creating a binary mask imagefrom an inputted digital image of a scanned document, comprising thesteps of: a) creating a binarized image by binarizing said inputteddigital image, b) detecting in said binarized image first text regionsrepresenting light text on a dark background in said inputted digitalimage, c) detecting in said binarized image second text regionsrepresenting dark text on a light background in said inputted digitalimage, d) inverting said first text regions in said binarized image,such that a transformed binary image is formed in which the invertedfirst text regions are interpretable in the same way as dark text on alight background, e) generating the binary mask image from thetransformed binary image by eliminating picture blobs that relate to noactual text elements, wherein the steps of detecting the first andsecond text regions and the step of inverting the first text regions areperformed in a single pass over the binarized image on the basis ofpixel blobs contained in the binarized image without analyzing if thefirst and second text regions represent text, wherein the pixel blobsare processed as soon as they are found and wherein descriptions of saidpixel blobs that are being used for said processing are eliminatedimmediately after said processing.
 2. The method according to claim 1,wherein the creation of said binary mask image further comprises thestep of eliminating from the binarized image text regions that representno actual text.
 3. The method according to claim 2, further comprisingthe steps of separating off horizontal and vertical graphical elementsbefore said steps of detecting first and second text regions, andreintroducing the said horizontal and vertical graphical elements intothe binarized image after said detection steps.
 4. The method accordingto claim 1, wherein step a) comprises the following steps: a1) buildinga grayscale image from said inputted digital image, a2) detecting edgesin said grayscale image, thereby building an edge binary imagecontaining edge pixels and non-edge pixels, a3) determining thresholdvalues for each of said edge pixels on the basis of surrounding pixelsand giving said non-edge pixels a null threshold value, thereby buildinga threshold grayscale image, a4) determining threshold values for eachof said non-edge pixels touching the edge pixels on the basis ofsurrounding threshold values, a5) scaling said threshold grayscale imageby keeping the maximum threshold values, a6) propagating the thresholdvalues from pixels having a positive value to pixels having a nullvalue, a7) building a first binary image on the basis of said grayscaleimage and said scaled threshold grayscale image.
 5. The method accordingto claim 4, wherein step a2) involves the use of a canny edge algorithmfor said edge detection.
 6. The method according to claim 4, whereinstep a) further comprises the following steps: a8) building a secondbinary image on the basis of said grayscale image and said thresholdgrayscale image, a9) building said binarized image by combining saidfirst and second binary images.
 7. The method according to claim 1,wherein said inputted digital image has a given resolution and saidcreation of said binary mask image involves reducing said resolution bya binary mask resolution reduction factor.
 8. A method for textrecognition, comprising the steps of: creating a binary image from aninputted digital image of a scanned document by means of the followingsteps: a. creating a binarized image by binarizing said inputted digitalimage; b. detecting in said binarized image first text regionsrepresenting light text on a dark background in said inputted digitalimage; c. detecting in said binarized image second text regionsrepresenting dark text on a light background in said inputted digitalimage; and d. inverting said first text regions in said binarized image,such that a transformed binary image is formed in which the invertedfirst text regions are interpretable in the same way as the second textregions; wherein the steps of detecting the first and second textregions and the step of inverting the first text regions are performedon a single pass over the binarized image on the basis of pixel blobscontained in the binarized image without analyzing if the first textregions represent text, wherein the pixel blobs are processed as soon asthey are found and wherein descriptions of said pixel blobs that arebeing used for said processing are eliminated immediately after saidprocessing; and applying a text recognition technique in which theinverted first text regions are recognized along with the second textregions.
 9. A compression method for compressing an inputted digitalimage of a scanned document, said compression method comprising thesteps of: a) segmenting said inputted digital image into multiple imagelayers comprising a foreground image containing color information forforeground elements of said document, a background image containingcolor information for background elements of said document and a binarymask image for selecting between pixels in said foreground image andsaid background image upon decompressing said compressed digital image,and b) compressing each of the image layers by means of a suitablecompression technique, thereby obtaining a compressed digital image,wherein creating the binary mask image involves the steps of: creating abinarized image by binarizing said inputted digital image, detecting insaid binarized image first text regions representing light text on adark background in said inputted digital image, detecting in saidbinarized image second text regions representing dark text on a lightbackground in said inputted digital image, inverting said first textregions in said binarized image, such that a transformed binary image isformed in which the inverted first text regions are interpretable in thesame way as dark text on a light background, generating the binary maskimage from the transformed binary image by eliminating picture blobsthat relate to no actual text elements, wherein the steps of detectingthe first and second text regions and the step of inverting the firsttext regions are performed in a single pass over the binarized image onthe basis of pixel blobs contained in the binarized image withoutanalyzing if the first and second text regions represent text, whereinthe pixel blobs are processed as soon as they are found and whereindescriptions of said pixel blobs that are being used for said processingare eliminated immediately after said processing.
 10. The methodaccording to claim 9, wherein said inputted digital image has a givenresolution and said creation of said binary mask image involves reducingsaid resolution by a binary mask resolution reduction factor.
 11. Themethod according to claim 9, wherein said inputted digital image has agiven resolution and said foreground and background images are built byreducing said resolution by respectively a foreground resolutionreduction factor and a background resolution reduction factor.
 12. Themethod according to claim 9, wherein step b) comprises the steps of: b1)compressing said foreground and background images by means of an imagecompression technique, b2) compressing said binary mask image by meansof a symbol-based compression technique.
 13. A computer program productstored on a non-transitory computer-readable medium and directlyloadable into a memory of a computer, comprising software code portionsfor performing the following steps when said product is run on acomputer: a) segmenting an inputted digital image of a scanned documentinto multiple image layers comprising a foreground image containingcolor information for foreground elements of said document, a backgroundimage containing color information for background elements of saiddocument and a binary mask image for selecting between pixels in saidforeground image and said background image upon decompressing saidcompressed digital image, and b) compressing each of the image layers bymeans of a suitable compression technique, thereby obtaining acompressed digital image, wherein creating the binary mask imageinvolves the steps of: creating a binarized image by binarizing saidinputted digital image, detecting in said binarized image first textregions representing light text on a dark background in said inputteddigital image, detecting in said binarized image second text regionsrepresenting dark text on a light background in said inputted digitalimage, inverting said first text regions in said binarized image, suchthat a transformed binary image is formed in which the inverted firsttext regions are interpretable in the same way as dark text on a lightbackground, generating the binary mask image from the transformed binaryimage by eliminating picture blobs that relate to no actual textelements, wherein the steps of detecting the first and second textregions and the step of inverting the first text regions are performedin a single pass over the binarized image on the basis of pixel blobscontained in the binarized image without analyzing if the first andsecond text regions represent text, wherein the pixel blobs areprocessed as soon as they are found and wherein descriptions of saidpixel blobs that are being used for said processing are eliminatedimmediately after said processing.